在规模上数字化纸张ECG:临床研究的开源算法
Elias Stenhede1,2, Agnar Martin Bjørnstad3,4, Arian Ranjbar3
1Medical Technology & E-health, Akershus University Hospital, Lørenskog, Norway. elias.stenhede@ahus.no.
NPJ digital medicine
|January 14, 2026
概括
本研究提出了一个自动化框架来数字化纸张心电图 (ECG),使历史的ECG数据可用于AI诊断. 该开源软件增强了扫描心电图的可用性,用于研究和临床应用.
科学领域:
- 生物医学工程 生物医学工程
- 医疗信息学 医疗信息学
- 计算机视觉 计算机视觉
背景情况:
- 数以十亿计的临床心电图 (ECG) 被保存为纸张扫描,这阻碍了它们在现代自动诊断系统中的使用.
- 数字格式的缺乏限制了广大的历史ECG档案的可访问性和分析潜力.
研究的目的:
- 开发和验证一个完全自动化的模块化框架,用于将扫描或拍摄的ECG转换为可用的数字信号.
- 改善用于临床和研究应用的ECG图像数字化的最新技术.
- 通过开源软件发布来促进可复制性和进一步开发.
主要方法:
- 为ECG图像处理开发一个模块化,自动化的框架.
- 通过37191张心电图像的大型数据集进行验证,其中包括来自Akershus大学医院和Emory Paper数字化心电图数据集的数据.
- 对图像的算法性能进行评估,这些图像具有各种人工制造物,如视角扭曲,纹和污点.
主要成果:
- 该框架在被扫描的ECG上实现了平均信号噪声比为19.65dB的平均信号噪声比.
- 与现有的最先进的方法相比,该模型在所有子类别中表现出优异的性能.
- 从各种来源成功数字化ECG,包括那些有显著的图像退化.
结论:
- 开发的框架有效地将扫描的心电图转换为数字信号,释放了回顾性心电图档案的潜力.
- 该软件的开源版本使人工智能驱动的诊断获得了民主化,并鼓励进一步的创新.
- 这项工作显著提高了ECG历史数据在临床实践和科学研究中的实用性.
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An electrocardiogram (ECG) is a diagnostic tool for identifying cardiac conditions such as arrhythmias, conduction abnormalities, and myocardial ischemia.
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